sqr_nyc <- 
  read_csv('./squirrel_data_in_nyc/squirrel-data.csv') %>% 
  janitor::clean_names() %>% 
  mutate(
    lat = squirrel_latitude_dd_dddddd,
    long = squirrel_longitude_dd_dddddd,
    lat_long = str_c('POINT (', long, ' ',lat,')'),
    activity = tolower(activities),
    unique_squirrel_id = squirrel_id
  ) %>% 
  mutate(
    primary_fur_color = case_when(
      primary_fur_color == "Gray" ~ "gray",
      primary_fur_color == "Cinnamon" ~ "cinnamon",
      primary_fur_color == "Black" ~ "black"
         )
    )%>%
  mutate(
    activity = map(str_split(activity, ','), 1) %>% unlist(),
    activity = map(str_split(activity, ' '), 1) %>% unlist()
      )%>% 
  mutate(
    activity = case_when(
      activity == "running" ~ "running",
      activity == "eating" ~ "eating",
      activity == "foraging" ~ "foraging",
      activity == "climbing" ~ "climbing",
      activity == "chasing" ~ "chasing",
          TRUE ~ 'others'
         )
    )%>%
  mutate(
    location = map(str_split(location, ','), 1) %>% unlist()
      ) %>% 
  mutate(
    location = case_when(
    location == "Ground Plane"~ "gound plane",
    location =="Above Ground" ~ "above ground",
    TRUE ~ 'others'
         )
    ) %>%
  dplyr::select(
    -c("squirrel_id" ,"color_notes", "activities",
       "other_notes_or_observations",  "squirrel_longitude_dd_dddddd",
          "squirrel_latitude_dd_dddddd")
    ) %>% 
  dplyr::select(unique_squirrel_id,everything()) %>% 
  filter(long < -70 )

sqr_nyc %>% skimr::skim_without_charts()
Data summary
Name Piped data
Number of rows 340
Number of columns 15
_______________________
Column type frequency:
character 13
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
unique_squirrel_id 0 1.00 7 7 0 340 0
area_name 0 1.00 8 17 0 4 0
area_id 0 1.00 1 1 0 4 0
park_name 0 1.00 11 31 0 18 0
park_id 0 1.00 2 2 0 18 0
primary_fur_color 1 1.00 4 8 0 3 0
highlights_in_fur_color 92 0.73 4 15 0 7 0
location 0 1.00 6 12 0 3 0
above_ground_height_in_feet 236 0.31 1 13 0 26 0
specific_location 271 0.20 4 48 0 28 0
interactions_with_humans 78 0.77 7 45 0 20 0
lat_long 0 1.00 26 28 0 324 0
activity 0 1.00 6 8 0 6 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100
lat 0 1 40.75 0.05 40.70 40.72 40.73 40.80 40.86
long 0 1 -73.97 0.03 -74.02 -74.00 -73.98 -73.94 -73.93

Squirrel Activity by Primary Fur Color

In our dataset, there are 5 main different kinds of activities reported which are foraging, running, eating, climbing and chasing. The first graph we drew about NYC data was to show the activities in squirrels by their different primary fur colors.

(plot_1 = 
  sqr_nyc %>%
  filter(!is.na(activity)) %>% 
  filter(!is.na(primary_fur_color)) %>% 
  group_by(primary_fur_color) %>%
  count(activity, sort = TRUE)%>%
  ggplot(aes(x = reorder(activity, n), y = n)) + 
  geom_bar(aes(fill = primary_fur_color), stat = "identity") + 
  scale_fill_manual(values = c("#000000", "#D2691E", "#D3D3D3", "indianred4")) +
  theme_classic() + 
  facet_wrap(~primary_fur_color, nrow = 1) +
  labs(title = "Squirrel Activity by Primary Fur Color", y = 'Number of Observations', x = 'Activity') +
  coord_flip())

Constrained on the activities indicated in the central park dataset, gray squirrels like to climb the most frequently and chase the least frequently. Black and gray squirrels like to forage the most and chase the least.

Squirrel Activity by Location of Squirrel

We make this plot to analyze central park squirrel activity by their location (above ground, ground plane). It was to show how distributions of different activities differ by the locations in the NYC.

sqr_nyc %>%
  filter(!is.na(activity)) %>% 
  filter(!is.na(location)) %>% 
  group_by(activity) %>%
  mutate(n=1) %>%
  ggplot() +
  geom_col(aes(y=n,x = activity, fill = location), position="fill") +
  ggtitle('Central Park Squirrels Acitivities by Location') +
  theme(plot.title = element_text(hjust = 0.5)) +
 theme(plot.title = element_text(hjust = 0.5), panel.grid.minor = element_blank(),panel.grid.major = element_blank())+ labs( x = 'Activity', y= 'Proportion')+
  labs(fill = 'Location') 

Activities like eating, foraging and running most happened on the ground plane, but other activities like chasing and climbing happened above ground for the most of time.

Primary Fur Color Map

We build this map to visualize the distribution of each squirrel by different primary fur color clearly.

pal_coats <- colorFactor(c("#000000", "#D2691E", "#D3D3D3", "white"), domain = c("black", "cinnamon", "grey"))

#Locations based on fur color
sqr_nyc %>%
  filter(!is.na(primary_fur_color)) %>%
  leaflet() %>%
  addTiles() %>%
  addCircleMarkers(lng = ~long,
                   lat = ~lat, radius = 3, color = ~pal_coats(primary_fur_color), stroke = FALSE, fillOpacity = 0.5) %>%
  addLegend(position = "topright",pal = pal_coats, values = ~primary_fur_color)